Prevent math overflow when loss is too high

    trainer.stop()

def get_perplexity(loss):

    try:

        return math.pow(2, loss)

    except OverflowError:

        return float('inf')

def train(args, epoch, batch_offset, trainer, dataset, num_gpus):

    """Train the model for one epoch."""

                save_checkpoint(trainer, args, epoch, i + 1)

        fmt = desc + ' | train loss {:2.2f} | train ppl {:3.2f}'.format(

            loss_meter.avg, math.pow(2, loss_meter.avg))

            loss_meter.avg, get_perplexity(loss_meter.avg))

        fmt += ' | s/checkpoint {:7d} | words/s {:6d} | words/batch {:6d}'.format(

            round(wps_meter.elapsed_time), round(wps_meter.avg), round(wpb_meter.avg))

        fmt += ' | bsz {:5d} | lr {:0.6f} | clip {:3.0f}%'.format(

        val_loss = loss_meter.avg

        fmt = desc + ' | valid loss {:2.2f} | valid ppl {:3.2f}'.format(

            val_loss, math.pow(2, val_loss))

            val_loss, get_perplexity(val_loss))

        fmt += ''.join(

            ' | {} {:.4f}'.format(k, meter.avg)

            for k, meter in extra_meters.items()